Clustering Lightning Discharges to Identify Storms

[This article was first published on Exegetic Analytics » R, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

A short talk that I gave at the LIGHTS 2013 Conference (Johannesburg, 12 September 2013). The slides are relatively devoid of text because I like the audience to hear the content rather than read it. The central message of the presentation is that clustering lightning discharges into storms is not a trivial task, but still a worthwhile challenge because it can lead to some very interesting science!

I evaluated both k-means and hierarchical clustering techniques but stuck with the latter because it was easier to formulate a dissimilarity matrix using great circle (as opposed to Euclidean) distances than to try and force the k-means algorithm to calculate geographic distances. In retrospect, I could have used pam() from the cluster package to do clustering around medoids (and which also uses a dissimilarity matrix). In addition, this would have the advantage of being somewhat more computationally efficient, but experimenting with that will have to wait for another day.

To leave a comment for the author, please follow the link and comment on their blog: Exegetic Analytics » R.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.